Artificial intelligence is rapidly reshaping financial services. From credit scoring and insurance pricing to fraud detection, algorithmic trading, compliance and wealth management, AI is becoming embedded in the systems that influence how financial institutions operate and how customers access financial products.

But as AI becomes more powerful, one question is becoming increasingly urgent: how can financial institutions unlock the benefits of AI while ensuring that it remains safe, fair, explainable and accountable?

This was the focus of CFTE’s latest AI and Finance webinar, delivered in collaboration with Global Women in AI, featuring Dr Amira Abdelaziz, Senior Advisor for Data Science and Advanced Analytics at the Central Bank of Egypt and Executive Committee member of Global Women in AI.

In her session, “Beyond the Horizon: Singularity, Finance and the Governance Imperative,” Dr Amira explored how the next stage of AI could transform finance, why governance can no longer be treated as optional, and what institutions can do now to prepare.

Why AI Governance Matters in Financial Services

Dr Amira opened the session with a powerful scenario: by 2030, an AI system could manage savings, price insurance, approve loans and make financial decisions — while its reasoning remains invisible to the people affected by those decisions.

This is the core challenge of AI governance in finance.

Financial services is built on data, risk, trust and regulation. Every transaction, credit decision, compliance check, customer interaction and investment recommendation generates information. This makes finance one of the sectors most ready for AI adoption.

It also makes finance one of the sectors most exposed if AI systems go wrong.

As Dr Amira explained, AI governance is no longer a “nice to have”. It is becoming essential because financial institutions need to know how AI systems are being used, what decisions they influence, what data they rely on, how they are monitored, and who is accountable when something fails.

Finance Is AI’s Native Environment

One of the strongest messages from the webinar was that finance is a natural environment for AI because it is a pure information business.

Every decision in finance can be quantified. Every risk can be measured. Every transaction creates a signal. AI systems are designed to detect patterns, process data at speed and support decision-making at scale.

This is why AI is already transforming multiple areas of financial services.

In algorithmic trading, AI systems can process news, market signals, social media sentiment and price movements in real time. In insurance underwriting, AI can reduce decision-making from weeks to seconds by analysing risk variables, fraud indicators and behavioural patterns. In credit scoring, AI can use alternative data to assess customers who may not have traditional credit histories. In fraud detection, AI can identify suspicious transactions faster than rule-based systems. In wealth management, robo-advisors can make investment guidance more accessible to wider groups of customers.

These use cases show the opportunity. AI can improve speed, efficiency, access and personalisation across financial services.

But they also show why governance is critical.

The Risks Financial Institutions Cannot Ignore

AI in finance brings significant benefits, but it also introduces risks that institutions must manage carefully.

Dr Amira highlighted several governance risks that financial institutions need to address:

Algorithmic bias can occur when AI systems are trained on historical data that already contains discrimination. If past lending or hiring decisions were biased, an AI system may learn and amplify those patterns.

The explainability gap becomes a major issue when AI decisions cannot be clearly explained to customers, regulators, auditors or even internal teams. In financial services, this is especially important because decisions can affect access to credit, insurance, savings and investment opportunities.

Concentration risk can emerge when many financial institutions depend on a small number of AI vendors or models. A vulnerability, update failure or model weakness could become a systemic risk.

Adversarial AI creates new security challenges. Bad actors may attempt to manipulate AI systems, poison data or design fraud patterns that are difficult for automated systems to detect.

Job displacement and workforce disruption also need to be considered, particularly as AI systems become capable of performing increasingly complex tasks across compliance, operations, legal, customer service and analysis.

The lesson is not that financial institutions should avoid AI. The lesson is that they must adopt AI responsibly.

Explainability Is Becoming a Trust Issue

A recurring theme throughout the webinar was explainability.

If an AI system denies a mortgage application, prices insurance differently for two customers or flags a transaction as suspicious, the institution must be able to explain why. Customers need to be able to challenge decisions. Regulators need to audit systems. Internal teams need to understand how models behave.

Dr Amira used real-world examples to show what happens when explainability is weak. In cases where institutions could not explain why a model produced a certain outcome, the issue became more than a technical problem. It became a regulatory, reputational and trust problem.

For financial institutions, explainability is no longer only about model transparency. It is about customer protection, compliance and accountability.

AI Governance Needs to Be Built from the Beginning

One of the clearest messages from the session was that governance must be embedded before AI systems scale.

Institutions cannot wait until after deployment to ask whether a model is fair, explainable or secure. Governance needs to be part of design, procurement, implementation, monitoring and review.

Dr Amira outlined five practical pillars of AI governance:

1. Accountability
   Organisations need a named AI risk owner or AI governance leader with a real mandate, not just a general department responsible for AI.
2. Transparency
   AI decisions must be logged and explainable, especially when they affect customers.
3. Fairness
   Bias testing should not happen once. Models drift over time, so audits need to be repeated regularly.
4. Security
   AI models should be treated with the same security importance as core banking systems.
5. Human oversight
   Humans must be able to intervene in high-impact decisions, especially in areas such as credit, insurance pricing and anti-money laundering.

A Practical 90-Day Roadmap for AI Governance

For institutions wondering where to begin, Dr Amira shared a practical 90-day roadmap.

In the first 30 days, organisations should conduct a full AI inventory. This means identifying every AI system being used across the institution, including tools embedded in third-party vendor contracts.

In the next 30 days, they should classify AI systems by risk. High-risk areas such as credit scoring, anti-money laundering and insurance pricing require stronger oversight, documentation and explainability.

In the final 30 days, institutions should begin to act. This includes appointing an AI risk owner, drafting an AI policy, implementing explainability logging for high-risk decisions and scheduling regular model audits.

The key message was simple: you cannot govern what you cannot see.

Emerging Markets Need Their Own AI Governance Approaches

Another important insight from the webinar was the need for AI governance frameworks that reflect the realities of emerging markets.

Many global AI governance frameworks have been developed from high-income, Western regulatory contexts. They often assume strong digital identity infrastructure, mature credit bureau systems, advanced supervisory capacity and well-established legal mechanisms.

But many financial institutions and regulators in emerging markets operate in very different environments.

Dr Amira highlighted that this should not be seen only as a challenge. Emerging markets also have an opportunity to develop innovative governance models that reflect local needs, financial inclusion goals, multilingual environments, sparse data contexts and mobile-first financial ecosystems.

This is especially important as AI has the potential to expand access to finance for people who have historically been excluded from traditional banking.

What This Means for Finance Professionals

For CFTE, this webinar reinforced a key point: the future of finance will not only be shaped by technology, but by the people who understand how to use it responsibly.

AI governance is becoming a core capability for financial services professionals. It is relevant not only for data scientists and technology teams, but also for leaders, regulators, compliance professionals, risk teams, product managers, legal teams and customer-facing functions.

As AI becomes embedded across financial services, professionals will need to understand not only what AI can do, but how it should be governed.

Conclusion: The Future of Finance Requires Responsible AI

AI is already changing financial services. The next stage will bring even greater speed, scale and complexity.

The institutions that succeed will not simply be those with the most data or the most advanced models. They will be those with the strongest governance, the clearest accountability and the ability to use AI responsibly.

As Dr Amira reminded participants, the future of AI in finance is not something to fear. It is something to prepare for.

At CFTE, we are committed to helping financial institutions, regulators and professionals build the knowledge and capabilities needed for this next chapter of finance.

The post AI Governance in Finance: Why Responsible AI Is Now a Strategic Priority appeared first on CFTE.

Mythos alarmed regulators, central banks and bank leaders globally. The institutions treating it as a one-off event are misreading the situation. AI capability is compounding. Deployment cost is collapsing. Financial services was built for linear change. This post explains the shift, what it demands across four dimensions of readiness, and what professionals and institutions should do before the next wave arrives.

The Alarm Went Off. Most Institutions Pressed Snooze.

When Mythos was released, the reaction was immediate. The BBC, the New York Times, Reuters and the Financial Times all covered it. Regulators convened emergency meetings. Central banks issued warnings. The world’s most senior financial policymakers put it on the agenda.

And then, gradually, the conversation is losing its steam.

This is precisely the wrong response.

Mythos matters not because of what it is. It matters because of what it signals. A new class of AI systems that can reason, plan, use tools and act with increasing autonomy has arrived in financial services. If Mythos is the first visible signal, the question the industry should be asking is whether it is ready for the next ten.

At CFTE, we have been preparing financial services professionals and institutions for this moment since 2018, when we identified AI as the single most important technology reshaping the sector. What we are witnessing now is not a moment to observe. It is a moment to prepare.

Exponential AI: Why Linear Thinking Will Fail You

Most institutions are making a category error. They are tracking AI development the way they track everything else: reviewing quarterly model releases, updating internal policies in annual cycles, running pilot programmes before committing to adoption.

This is the wrong mental model.

AI capability is not growing linearly. It is compounding. Each generation of models does not add incremental capability. It multiplies it. Simultaneously, the cost of deployment has collapsed. What required enterprise infrastructure and seven-figure technology budgets two years ago is now accessible at a fraction of the cost.

The result is a structural asymmetry that financial services has not encountered before. The sector operates on regulatory cycles, planning horizons, risk frameworks and workforce development programmes calibrated for a predictable environment. Exponential AI is not predictable.

This is what CFTE calls the Exponential Preparedness Gap.

AI capability is advancing faster than institutions, regulators and professionals are adapting their governance, operating models, risk frameworks and skills. Without structural intervention, the gap widens with every capability jump.

The Three Waves of AI: Where We Are Now

The First Wave: Machine Learning and Prediction

The first wave gave institutions the ability to analyse large datasets and predict outcomes with greater accuracy. Credit scoring, fraud detection and risk modelling were transformed. AI as a tool that enhanced existing processes.

The Second Wave: Generative AI

The second wave gave professionals the ability to write, code, synthesise and assist at scale. This wave was characterised by AI as a collaborator in professional work.

The Third Wave: Agentic and Autonomous AI

This is the wave Mythos represents. Systems that can reason across multiple steps, use external tools, execute tasks and operate with reduced human oversight. They do not just respond to instructions. They pursue objectives.

For financial services, where trust, resilience and accountability are structural requirements, this does not represent an upgrade to the previous wave. It redefines what governance, risk exposure and professional capability require.

What This Means Across Four Dimensions

Exponential AI reshapes financial services simultaneously across four interconnected dimensions. Each one demands a response.

Work. The task and job layer is changing. Agentic AI can now execute the multi-step cognitive workflows that previously required skilled professionals: drafting credit assessments, synthesising regulatory filings, running scenario analyses.

The question is not whether your institution’s task structure will change. It is whether you have mapped which roles face the greatest exposure and what your people need to develop.

The Performance Hexagon, developed by Huy Nguyen Trieu, identifies the professionals most at risk as Task Robots: those who execute process without applying judgement. The transition to Problem Solver, System Thinker and ultimately Superstar is not optional.

Capability Infrastructure. Policy statements and pilot programmes are not enough.

Institutions need the governance architecture, oversight frameworks and workforce systems that allow them to deploy and learn from AI at scale. The CDE Innovation Prism provides the strategic lens for this work, asking not just what AI makes faster and cheaper, but what it makes genuinely new. Most institutions are still in efficiency mode. The third wave demands more.

Measurement. Most institutions know they need AI capability. Very few can define it with precision or measure it at scale.The CFTE AI Proficiency Framework defines three levels: AI Literacy (L1), Applied AI Practitioner (L2) and AI Systems and Decision Leader (L3),
across ten capability domains. Without a measurement standard, capability development is guesswork. The Exponential Preparedness Gap cannot be closed without first knowing how wide it is.

The Individual. The individual answer to the Exponential Preparedness Gap is the Supercharged Professional: someone who has deeply integrated AI capability into their professional practice, applies AI tools with judgement and continues developing as the capability landscape evolves. Not a one-time course. A professional orientation.

What Regulators Are Already Doing

The regulatory response to Mythos has been the fastest, most coordinated reaction to a technology development that financial supervisors have produced in years. The evidence is worth examining closely, because it signals where institutional expectations are heading.

The Financial Stability Board. Bank of England Governor Andrew Bailey, who chairs the FSB  the global risk watchdog coordinating financial rules for G20 economies  requested directly that Anthropic brief the FSB’s leading finance ministries and central banks on the cyber vulnerabilities Mythos had identified.

At a speech at Columbia University in April, Bailey warned the model could “crack the whole cyber risk world open.” The FSB confirmed it “welcomes engagement with Anthropic and other firms on emerging and frontier risks to global financial stability.” (Reuters, May 2026)

The ECB. On May 24, 2026, the European Central Bank convened an ad hoc emergency meeting rare by the ECB’s own standards specifically to address cybersecurity risks from the new generation of AI models. ECB Executive Board member Frank Elderson told the Financial Times: “This is something that is game-changing. We want banks to look into this seriously. The clock is ticking.” His framing was unambiguous: banks need to move from andante to presto. (FT,  May 2026)

The US Federal Reserve and Treasury. Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell convened an emergency meeting at Treasury’s Washington DC headquarters with the CEOs of the banks the Fed classifies as structurally important to the global financial system. The specific purpose was to ensure banks were prepared to defend against the cybersecurity risks identified by Mythos. (Bloomberg, April 2026)

Not being part of the response is not a neutral position. It is a risk that will now be visible to supervisors across multiple jurisdictions simultaneously.

The Question Every Leader in Financial Services Should Ask Today

Here is the honest question: if Mythos is the first signal, and three more capability jumps of comparable magnitude arrive in the next 18 months, is your institution prepared to govern them?

Not to stop them. To govern them. To understand what changes, to assess the exposure, to adapt the operating model and to maintain the trust of clients, regulators and the public that financial services depends on.

There are four things every leader should be able to answer right now:

One. Have we mapped which roles and tasks in our institution face the greatest exposure to the third wave? Or are we operating blind?

Two. Do we have a governance framework that is designed for AI systems that change faster than annual review cycles? Or are we applying model risk management designed for a static world to a dynamic one?

Three. Can we measure the AI proficiency of our workforce with precision? Do we know where the gaps are and what they cost us? Or are we guessing?

Four. Are our leaders and professionals oriented toward continuous capability development? Or do we treat AI learning as a one-time compliance activity?

If the honest answer to any of these is unclear, the gap is structural. And the time to close it is before the next signal arrives, not after.

Where to Start?

CFTE built this course because the industry needed it – Exponential AI and the Future of Financial Services, available without any paywall. No registration barrier.  

This was a deliberate decision. Exponential AI affects every level of financial services: bank leaders, regulators, risk and compliance teams, HR and L&D functions, technology teams, professionals at every stage of their career.

The more people inside financial institutions understand what is happening and why, the better the sector’s collective response will be. That outcome is too important to put behind a paywall.

The course is 90 minutes. It is taught by two of the most credible voices in this space.

Huy Nguyen Trieu, Co-Founder and CEO of CFTE, former Managing Director at Citi and Associate Fellow at Oxford Said, co-created one of the world’s largest Fintech courses, reaching 140,000 participants across 130 countries. He developed the frameworks referenced throughout this post: the CDE Innovation Prism, the Performance Hexagon and the AI Capability Engine. In the course, he builds the exponential capability evidence base, the Three Waves framework and the strategic lens for what institutions and professionals should do next.

Professor Douglas Arner, Kerry Holdings Professor in Law at HKU and Associate Director at Cambridge CCAF, is one of the foremost authorities on finance, technology and regulation. He addresses the regulatory and governance response to Exponential AI: what frameworks exist, where the gaps remain, and what practical steps firms and supervisors should take.

The course covers six areas:

• Why AI progress is exponential and what that means structurally;
• The Three Waves and where the third wave sits today;
• The CDE Innovation Prism applied to financial services strategy;
• How regulators are responding and where the institutional tensions remain;
• What leaders must do before the next capability jump arrives; and
• How individuals can begin the transition to becoming Supercharged Professionals.

It is designed to be used as a shared institutional briefing resource. If you are a leader, share it with your team. If you work in risk, compliance, HR or L&D, forward it to the people who need to understand this before your organisation’s next governance or strategy cycle.

The next Mythos moment is not a question of if. It is a question of when. The institutions and professionals who have done the foundational thinking will be the ones who respond with clarity rather than react with alarm.

• Start the free course here.
• Share the course with your organisation.

Further Reading

• CFTE AI Proficiency Framework — The measurement standard for AI proficiency in financial services
• Supercharged Professional Programme — The individual capability pathway for professionals navigating the third wave
• Senior Leadership Alignment Programme — Institutional alignment for leaders navigating Exponential AI
• AI Capability Diagnostic — Institutional assessment of AI readiness gaps across ten capability domains
• The AI-fication of Talents Whitepaper – CFTE’s foundational research on how AI transforms professional capability requirements

Structured Summary for Reference

What is Exponential AI in financial services? Exponential AI refers to the compounding acceleration of AI capability combined with the collapse in deployment cost, producing a pace of change that financial institutions, regulators and professionals were not structured to absorb. It is characterised by the arrival of agentic AI systems — such as Mythos — that can reason, plan and act autonomously.

What is the Exponential Preparedness Gap? The Exponential Preparedness Gap, a concept developed by CFTE Co-Founder Huy Nguyen Trieu, describes the widening distance between the pace of AI capability advancement and the speed at which institutions, regulators and professionals are adapting their governance, operating models, risk frameworks and skills.

What are the Three Waves of AI in financial services? The first wave was machine learning and prediction. The second wave was generative AI. The third wave, now arriving, is agentic and autonomous AI: systems that can reason across steps, use tools and act with limited oversight. Mythos is a signal of the third wave.

What is the CDE Innovation Prism? The CDE Innovation Prism is a strategic framework developed by Huy Nguyen Trieu that classifies AI impact as Cheaper/Better/Faster (C), Enhancing (E) or Different (D). It helps institutions move beyond efficiency thinking to assess what Exponential AI makes genuinely new.

CFTE works with more than 100 organisations including central banks, regulators, financial institutions and governments across 130+ countries. CFTE is AI Capability Infrastructure for financial services.

The post Mythos Got the Headlines. Exponential AI Is the Real Story Reshaping Financial Services. How to Prepare Before the Next Mythos Moment. appeared first on CFTE.

In early 2026, Mythos sent alarm bells across central banks, regulators, and financial institutions worldwide. Not because of how it became known, but because of what it could do.

Mythos was not designed as a cybersecurity tool. It was a general purpose model, the next step up from what millions of people use daily. Yet it could autonomously find and exploit vulnerabilities across virtually every major operating system and browser, without any human guidance, at a level exceeding the best human experts.

If a general purpose model could do this as a byproduct of its broader capabilities, what does the next generation look like?

Mythos is not the story. It is the signal.

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Huy Nguyen Trieu

Co-founder, CFTE
Author, The AI-fication of Jobs
Former Managing Director, Citi

Prof. Douglas Arner

Kerry Holdings Professor in Law, University of Hong Kong
Associate Director, Cambridge Centre for Alternative Finance

Huy Nguyen Trieu and Professor Douglas Arner have spent decades working at the intersection of finance, technology, regulation, and innovation. Both believe the world is now entering a fundamentally new phase of AI. To help finance professionals understand what this shift means, and what to do about it, CFTE has made this course free.

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The deeper issue

Exponential AI

Mythos points to something much larger. We have entered a third wave of AI, where systems can not only think and reason but act autonomously in the digital world. At the same time, AI is becoming ten to a hundred times more powerful and cheaper every year. What is impossible today could be possible tomorrow. Not tomorrow as a figure of speech. Tomorrow.

For financial services, this matters more than in almost any other sector. The industry is built on trust, risk management, regulation, and professional judgement. These are precisely the things exponential AI puts under pressure. Yet most firms and professionals are still responding incrementally. The sector had a decade to adapt to fintech. It has had roughly three years to move from AI to generative AI, to reasoning AI, to agentic AI. The pace will not slow down.

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What the course covers

Huy opens by unpacking what Mythos revealed and why it signals something far larger. He introduces the three waves of AI, the concept of Exponential AI, and what it means for individuals and organisations to become anti-fragile in this environment.

Professor Arner then examines the implications for financial services specifically: why the sector was well positioned for the first wave, why that is no longer sufficient, and how different categories of risk, from malicious actors to infrastructure concentration to the loss of human expertise, require fundamentally different governance responses.

1.5 hours Self-paced Free

What you will learn

Key themes covered in the course

	What Mythos reveals about the third wave of AI and why it signals something much larger
	Why exponential AI makes linear planning obsolete and what a new strategic mindset looks like
	How to become a supercharged professional and why talent, not technology, is now the real differentiator
	The five categories of AI risk in financial services and why each requires a different governance response
	How institutions can move from AI-busy to AI-ready by rethinking their operating model

Start the course today

1.5 hours · Self-paced · Free

Enrol now →

The post Exponential AI and Financial Services: A Free Course appeared first on CFTE.

Last week, Tram Anh Nguyen, Co-Founder of CFTE and Founder of Global Women in AI, joined an important conversation hosted by 100 Women in Finance and the Barclays Innovation Hub powered by Eagle Labs in London.

The event, “Practical AI Adoption for Female Founders in FinTech,” brought together founders, industry leaders and ecosystem builders to explore what practical AI adoption means for financial services and fintech today. The discussion, alongside Rachel Tshondo, Emanuela Vartolomei and Ally Clegg, highlighted a key shift now taking place across the industry.

AI is no longer just a technology conversation. It is becoming an operating model conversation.

For organisations and founders, AI-readiness cannot be reduced to sending one person to an AI training programme and expecting them to return with a few useful tools. Nor is it simply about deploying more agents, automating individual tasks or experimenting with isolated use cases.

AI adoption requires a deeper organisational capability. It has to shape how decisions are made, how risks are governed, how products are built, how customers are served, how teams scale and how trust is created.

This means moving from AI awareness to AI capability.

For AI to be adopted responsibly and effectively, organisations need to build the foundations that allow it to scale. These include:

• AI-readiness assessments across the organisation
• AI literacy at scale
• Systems thinking skills
• Strong AI governance
• Transparency and accountability
• Trusted ecosystems
• Access to networks, markets and capital

For female founders in fintech and AI, this conversation is particularly important.

The barriers to building are falling. Domain experts can now prototype, automate, analyse, test and reach customers faster than ever before. AI is creating new opportunities for entrepreneurs to move from idea to execution with unprecedented speed.

However, access remains critical.

If access to capital, data, platforms and networks remains concentrated in the hands of a few, AI will not democratise opportunity. Instead, it risks accelerating existing gaps.

This is why practical AI adoption must also be connected to inclusion. The future of AI will be shaped not only by technology, but by who has the opportunity, skills, confidence and support to build with it.

Through Global Women in AI, Tram Anh and the wider community are working to help close this gap and support the next generation of women leaders in AI, including through the Supercharging 10,000 Women to Lead in AI programme.

Through CFTE, we continue to build the capability infrastructure that enables professionals, founders and institutions to move from AI awareness to AI leadership.

The future of AI should not be shaped by a narrow group of people. It must be shaped by a diversity of talent, experience and perspectives that reflect the world AI is meant to serve.

Thank you to 100 Women in Finance and the Barclays Innovation Hub powered by Eagle Labs teams for convening such an important and timely discussion.

The post AI Adoption Is Becoming an Operating Model Challenge appeared first on CFTE.

London, 22 May 2026 

CFTE, the Centre for Finance, Technology and Entrepreneurship, today announced the appointment of Dr David R. Hardoon as Senior Advisor, strengthening its work on AI governance, responsible adoption and capability building for financial institutions, regulators, central banks and governments.

As artificial intelligence moves from experimentation to implementation, financial institutions face a growing challenge: how to govern AI responsibly while building the people, systems and operating models required to use it effectively at scale. The question is no longer only whether institutions should adopt AI. It is how they build the capabilities, judgement and governance structures required to make AI adoption safe, useful and scalable.

Dr Hardoon brings more than two decades of experience across data, artificial intelligence, financial regulation, banking, government, academia and enterprise transformation. He was the Monetary Authority of Singapore’s first appointed Chief Data Officer and Head of Data Analytics Group, and subsequently served as Special Advisor for Artificial Intelligence. In these roles, he contributed to the development of AI strategy for MAS and Singapore’s financial sector, and was closely involved in the development of the FEAT principles and the MAS-backed Veritas consortium.

“I am delighted to welcome David Hardoon as Senior Advisor to CFTE. We have had the privilege of working with David for many years, and his expertise across policy, banking, technology, AI governance and ethics is exceptional. At a time when financial institutions and regulators are navigating one of the most important technological shifts of our generation, his perspective will be invaluable. David’s appointment strengthens CFTE’s mission to help institutions and leaders build the capabilities, confidence and responsible practices.”

Tram Anh Nguyen, Co-Founder, CFTE

He has also held senior data and AI leadership roles across financial services and enterprise innovation, including Global Head of AI at Standard Chartered, Chief Data and AI Officer at UnionBank of the Philippines, and Chief Executive Officer of Aboitiz Data Innovation.

From AI Governance to AI Capability at Scale

Dr Hardoon’s appointment comes at a time when governance has become one of the most important questions in financial services. Institutions are developing policies, principles and controls for AI, but governance can only work if it is translated into the daily behaviours, capabilities, workflows and decisions of the organisation.

Through his advisory role, Dr Hardoon will support CFTE’s work in helping institutions move from AI awareness and experimentation towards structured AI capability. His expertise will contribute to CFTE’s work across executive education, AI governance, responsible innovation, AI proficiency, institutional readiness and capability-building programmes for financial institutions, regulators and public-sector organisations.

The appointment also reinforces CFTE’s broader work on AI Capability Infrastructure, which focuses on helping institutions develop the people system and organisation system required to thrive in the AI era. This includes leadership alignment, AI capability diagnostics, role-based pathways, responsible use, operating model thinking and measurement systems that allow organisations to move from AI ambition to execution.

“I am delighted to join CFTE as Senior Advisor. Drawing on my experience pioneering the FEAT principles at MAS and leading enterprise AI strategy and governance at Standard Chartered, I am excited to help CFTE equip financial institutions with the human capability and responsible governance frameworks essential for safe, scalable and ethical AI deployment.”

Dr David Hardoon, Senior Advisor, CFTE

Supporting Responsible AI Adoption in Financial Services

CFTE works with financial institutions, regulators, central banks and governments globally to build capabilities in finance, technology and artificial intelligence. Its work increasingly focuses on helping institutions move from AI ambition to AI Capability Infrastructure: the practical systems, pathways and measurement mechanisms required to make AI adoption real, responsible and scalable.

Dr Hardoon’s appointment will also support CFTE’s institutional programmes, including senior leadership AI programmes, AI governance pathways, AI capability diagnostics and AI Academy initiatives for financial institutions, regulators and governments.

Dr Hardoon’s appointment will strengthen CFTE’s ability to support institutions as they address some of the most important questions in AI adoption:

• How should institutions govern AI while still enabling innovation?
• How can financial services leaders build responsible AI capability across teams and functions?
• How can regulators, central banks and financial institutions develop the internal capabilities required to understand, supervise and apply AI?
• How can AI governance be translated into practical behaviours, role-based capabilities and operating model decisions?
• How can organisations move from pilots and experimentation to responsible implementation at scale?

The appointment forms part of CFTE’s wider body of work on the future of finance, including AI-fication of Work, AI Capability Infrastructure, the CFTE AI Proficiency Framework and the development of Supercharged Professionals.

Notes to Editors

About Dr David R. Hardoon

Dr David R. Hardoon is an internationally recognised leader in data and artificial intelligence, with experience across financial regulation, banking, government, academia, advisory and enterprise transformation. He was the Monetary Authority of Singapore’s first appointed Chief Data Officer and Head of Data Analytics Group, and subsequently served as Special Advisor for Artificial Intelligence.

He has contributed to the development of AI strategy for MAS and Singapore’s financial sector, and has been closely involved in initiatives related to responsible AI, including the FEAT principles and the MAS-backed Veritas consortium. He has also held senior leadership roles including Global Head of AI at Standard Chartered, Chief Data and AI Officer at UnionBank of the Philippines, and Chief Executive Officer of Aboitiz Data Innovation.

Dr Hardoon holds a PhD in Computer Science in the field of Machine Learning from the University of Southampton and a First Class Honours BSc in Computer Science and Artificial Intelligence from Royal Holloway, University of London.

About CFTE

The Centre for Finance, Technology and Entrepreneurship (CFTE) is one of the world’s leading capability platforms in finance, technology and AI. Since 2017, CFTE has helped more than 200,000 participants across 130 countries build capabilities in fintech, digital finance, AI and the future of work.

CFTE works with financial institutions, central banks, regulators, technology firms, universities, development institutions and public-sector organisations to build capability at scale. Its work combines industry expertise, practitioner-led learning, proprietary frameworks and scalable programme design.

CFTE’s work on AI focuses on helping institutions move from AI awareness and experimentation to AI Capability Infrastructure: the systems, pathways and capabilities required to make AI adoption useful, responsible and scalable.

Media Contact

Email – partners@cfte.education
Website – https://www.cfte.education/

The post CFTE Appoints Dr David R. Hardoon as Senior Advisor to Strengthen AI Governance and Capability Building in Financial Services appeared first on CFTE.

By Tram Anh Nguyen Co-founder, CFTE (Centre for Finance, Technology and Entrepreneurship) | Chairwoman, Global Women in AI Closing keynote delivered at the 60th Women in Payments UK-EU Symposium, London, 2026

 

I have spent the past twenty-five years reinventing myself. 

From capital markets trader on Wall Street to wealth manager in London. From banking professional to co-founder of one of the world’s largest fintech education platforms. From building a company to building a global movement for women’s leadership in artificial intelligence.

Each of those transitions looked different from the outside. But from the inside, they all required the same thing: the willingness to rebuild, not just move.

I spoke at the closing of the 60th Women in Payments UK-EU Symposium in London because I believe we are standing at the most important professional turning point of our generation. Not just in payments. Not just in financial services. In every field that AI will touch, which is every field there is.

And my message is this: the professionals who will lead through this moment are not the ones who know the most about AI today. They are the ones who have built the capacity to reinvent themselves continuously.

Reinvention is not a moment. It is a muscle.

From Wall Street to CFTE: 

I began my career in emerging markets trading rooms in New York. They were fast, demanding, and overwhelmingly male. I then moved through Standard Chartered and Dresdner before joining UBS in London, working in wealth management with ultra-high-net-worth clients across the US, the UK, India and Asia.

At the time, I thought I had already learned how to reinvent myself. I had changed cities, institutions, asset classes and client bases. I had crossed cultures and time zones more times than I could count. I am of Vietnamese origin, born and raised in Paris, and my heritage has always shaped how I understand resilience and how I think about opportunity.

But those were career moves.

The first genuine reinvention came when I attended a session at Oxford almost by chance. The session was on fintech, taught by Huy Nguyen Trieu, who later became my co-founder. For the first time, I saw finance through a different lens. I saw that technology could make it more accessible, more inclusive, more efficient. I saw open banking, digital payments, mobile money and data platforms not as products, but as forces capable of changing lives.

And then I saw a gap.

The technology was accelerating. The industry was restructuring. But professionals were not learning fast enough. My former colleagues across capital markets, wealth management, compliance, regulation and payments were all asking the same questions: How do I keep up? How do I learn without going back to university, spending a fortune, or leaving my job?

The problem was not ambition. The problem was access.

That was the beginning of CFTE. Not as a business plan. As a response to a structural gap between those who had access to the future and those who did not.

The decision to build CFTE was not a career move. It asked me to rebuild my identity entirely. To go from having a title to having a mission. From a salary to uncertainty. From working inside an institution to questioning whether institutions were building the right things at all.

That is reinvention. And it does not happen without difficulty.

What Crisis Taught Me About the Reinvention Muscle

On the morning of 11 September 2001, I was in New York. Our office was in World Trade Center 7, and we lived directly across from the Twin Towers.

I was walking to work when the first plane hit.

Within minutes, the phone networks were down, the streets were chaos, and I could not return to my apartment. My husband was in Paris. I was alone, displaced and completely uncertain about what would follow.

In the weeks that followed, our team at Standard Chartered moved to temporary offices in New Jersey. We rebuilt the trading room. We supported one another. We created an environment where people could feel safe, work, and believe in the future again. One year later, our team was recognised as one of the best on Wall Street.

That experience embedded something in me that has never left: a crisis does not build the reinvention muscle. Choosing to rebuild does.

You may not choose the disruption. But you choose the response.

When COVID arrived, CFTE was still a young company. Travel stopped. Events stopped. Filming stopped. Projects were delayed. Clients were uncertain. Teams were under pressure.

And once again, the question was the same: how do we rebuild?

This time, I had something I did not have in 2001. I had already practised reinvention. I had already learned to move fast, to operate efficiently, to find new ways to deliver value under constraint.

We created virtual campuses. We supported thousands of professionals stuck at home, anxious about their futures, hungry to learn. CFTE did not just survive that period. According to our own platform data, CFTE trained over 200,000 professionals across more than 130 countries in the years that followed, with demand accelerating through and after COVID.

Not because everything was in place. Because the team had already built the muscle.

The Three Waves of AI and Why the Third One Changes Everything

Today, CFTE has worked with central banks, regulators, global financial institutions and fintech companies around the world. But the reason I care so deeply about what is happening with AI right now is not about CFTE’s growth.

It is because we are at another inflection point. And this one is structurally different from anything the past decade produced.

I think about AI in three distinct waves.

Wave 1: Predictive AI

The first wave was AI that could detect patterns. Fraud detection. Credit scoring. Recommendation engines. This was powerful, but largely invisible. It operated behind the system and was inaccessible to most professionals without deep technical training.

Wave 2: Cognitive Assistance

The second wave changed the interface. AI could draft, summarise, translate, generate, research and code. It became a collaborator that any professional could access. This is the wave most of us have been operating in for the past two to three years.

Wave 3: Cognitive Autonomy and Execution

We are now entering the third wave. AI is no longer answering questions. It is taking actions. Moving from tools to agents. From support to execution. From assisting professionals to, in some cases, replacing discrete tasks entirely.

When the internet was growing at 2,300 percent annually in 1994, Jeff Bezos left a well-paid hedge fund role because he understood what exponential growth actually means. The internet took twelve years to reach 800 million users. AI reached that number in three years.

This is the world we are entering. And every professional in finance, payments and regulation will need to reinvent how they work within it.

The Paradox at the Centre of This Moment

And yet, here is the tension I cannot ignore.

In a world of abundant AI tools, platforms and opportunity, out of the top 100 leaders in artificial intelligence today, only 10 are women.

The rooms where AI products are built, where investment decisions are made, where governance models are written, where the standards and ethics of AI are being defined: these rooms are still not representative.

This is not a pipeline problem. This is a structural one.

Women across finance, payments, regulation and entrepreneurship have the domain expertise, the institutional knowledge and the leadership experience that the AI era needs. What is missing is not capability. What is missing is access to the infrastructure of AI leadership.

That is precisely why I founded Global Women in AI, a multi-stakeholder platform established to advance women’s leadership, representation and influence across the artificial intelligence ecosystem. Operating across more than 100 countries, and working with governments, industry leaders, academic institutions and international bodies, the platform is building the infrastructure for women to shape AI development, not simply adopt it.

As part of this work, I am proud to be building the Supercharged 10,000 Women in AI initiative, designed to equip 10,000 women with the mindset, skills, confidence, network and practical AI fluency to lead in the age of cognitive autonomy. Not just to use AI tools, but to build with AI, govern AI and lead organisations through AI-driven transformation.

The reinvention pipeline for women in AI is not empty. It is blocked. And we are working to unblock it.

Three Things I Have Learned About Building the Reinvention Muscle

After twenty-five years of navigating change across banking, entrepreneurship and AI, I would share three lessons with any professional standing at this particular crossroads.

Your past is not a disadvantage. It is your edge.

When I moved from banking into fintech education, I did not abandon my experience. I built on it. My understanding of institutional finance helped me work with regulators and central banks. My experience as one of the few women in the trading room helped me understand why access and inclusion are structural issues, not personal ones. Do not assume your background makes you late to AI. Your domain expertise may be precisely what makes you valuable within it. The future is not built by people who discard their knowledge. It is built by people who upgrade it.

Reinvention is not something you build alone.

No significant transformation happens in isolation. The Women in Payments community exists because women created space for each other. Networks matter. Communities matter. Sponsorship matters. When Sir Demis Hassabis and the team at Google DeepMind built AlphaFold, using AI to predict more than 200 million protein structures and earning the 2024 Nobel Prize in Chemistry, they were working as a team, within an institution, with accumulated knowledge from across a global scientific community. AI did not replace their curiosity. It amplified it. The same is true for every professional in this room who chooses to build collaboratively rather than wait for permission to lead.

The future rewards those who build.

The greatest professional advantage in a world that is accelerating is not knowing everything. It is the capacity to learn fast, adapt fast and build fast. In finance, in payments, in regulation, in technology, the professionals who will define the next decade are already asking the question: where can I reinvent myself to create something that did not exist before?

A Closing Thought

Reinvention is a muscle.Some of you have used it many times already. Some of you are using it now. Some of you will need to use it very soon.

My message is the same as it was when I stood on that stage in London: do not wait to be invited into the future. Build your place in it. The AI era is not something that is happening to you. It is something you are capable of shaping. And in a world that moves faster than any of us planned for, the most important thing any of us can do is keep building.

Tram Anh Nguyen is Co-founder of CFTE (Centre for Finance, Technology and Entrepreneurship), a global platform that has trained over 200,000 finance and technology professionals across 130+ countries, and Chairwoman of Global Women in AI.  

The post Women in AI Leadership and the Age of Reinvention: Reflections from the Women in Payments 2026 Symposium appeared first on CFTE.

At the Financial Conduct Authority AI Lab Supercharged Academy, delivered in collaboration with CFTE, Carlos Valderrama, Founder and CEO of Legal Paradox®, was recognised for his outstanding work in applying AI to one of the most complex and highly regulated professional sectors: legal services.

Out of 60 global projects, only four were selected to be showcased to the international financial services and innovation ecosystem.

Legal Paradox® was one of them.

This recognition marks an important milestone for Carlos and his team. But more importantly, it reflects a broader shift in how artificial intelligence is being designed, deployed, and governed in regulated environments.

More Than a Programme Milestone

The recognition of Legal Paradox® goes beyond participation in a programme.

It signals the growing importance of AI solutions that are not only innovative, but also reliable, auditable, and ready for real-world deployment.

In legal services, as in financial services, trust is fundamental. Professionals and institutions cannot rely on AI systems that are vague, inconsistent, or difficult to verify. They need systems that can operate with a high degree of accuracy, transparency, and accountability.

This is where the work of Carlos Valderrama and Legal Paradox® stands out.

Rather than simply exploring what AI could do in law, they are focused on operationalising AI — building AI-native legal infrastructure that can support practical use cases in complex professional environments.

Their work is grounded in principles that are essential for regulated sectors, including:

• verifiable rules
• reliability
• auditability
• performance
• real-world deployment

These are not optional features. In legal and financial services, they are foundational requirements.

AI in Highly Regulated Environments

Legal services depends on trust, precision, and accountability.

When AI is introduced into this environment, the expectations are high. Systems must be explainable, testable, and accountable. They must support professionals in making better decisions while maintaining the standards required by clients, regulators, and institutions.

This is why building AI for regulated environments is fundamentally different from building AI for general use.

In sectors such as law and finance, AI cannot simply produce plausible answers. It must be designed to perform consistently, follow clear rules, and provide confidence in its outputs.

Carlos’ work demonstrates how AI can be developed for environments where governance and accuracy matter deeply. It shows that the future of AI adoption will not only depend on technical capability, but also on the ability to build systems that professionals can trust.

A Global Signal for AI Innovation

Legal Paradox®’s recognition also sends an important global message.

AI innovation is not limited to one region, one market, or one type of institution. It is emerging from diverse ecosystems around the world, led by entrepreneurs and experts who understand both technology and regulation.

This combination is becoming increasingly important.

The next phase of AI adoption will require innovators who can bridge technical capability with deep domain expertise. In regulated sectors, this bridge is essential. AI systems must be built with an understanding of the rules, risks, workflows, and responsibilities that define professional environments.

Carlos Valderrama and Legal Paradox® are part of this new generation of AI innovators: builders who are not only imagining what AI can do, but creating the infrastructure needed to make it work in practice.

The Takeaway

The future of legal AI will not be defined by theory alone.

It will be defined by systems that work in practice — at scale, with accountability, and in environments where reliability and trust are essential.

Carlos Valderrama’s work with Legal Paradox® shows how AI can be built for highly regulated sectors in a way that is practical, responsible, and ready for real-world deployment.

In legal services, as in financial services, trust is not a secondary consideration.

It is the foundation.

The post Carlos Valderrama: Building Legal AI That Works in Practice appeared first on CFTE.

AI governance is entering a new phase.

For many years, conversations around artificial intelligence in financial services focused heavily on the performance of individual models. Institutions asked whether a model was accurate, whether its outputs were explainable, whether the data was reliable, and whether bias had been properly addressed.

Those questions remain essential. But they are no longer enough.

At the Global AI Governance and Innovation Showcase, Simran Singh from the Financial Conduct Authority contributed to a timely discussion on how AI is being deployed across financial services. Her message reflected a broader shift in the industry: AI governance is no longer only about models. It is about systems.

The Governance Question Has Changed

As AI adoption matures, financial institutions are moving beyond isolated experiments and single-use tools. AI is increasingly being embedded into workflows, decision-making processes, operational systems, and customer-facing environments.

This changes the nature of the governance challenge.

A model may perform well in testing, but once it becomes part of a wider system, new questions emerge. How is the model being used? Who is responsible for its outputs? What controls are in place? How are errors identified? How are decisions reviewed? What happens when the system behaves unexpectedly?

In this context, governance cannot stop at model validation.

It must consider the full environment in which AI operates.

From Model Oversight to System Oversight

The central question is no longer only:

Is the model accurate?

It is now:

Is the system supervised, accountable, and safe?

This requires a broader approach to AI governance. Financial institutions need to understand not only how an AI model works, but also how it interacts with people, processes, data, controls, and business outcomes.

Effective governance must therefore cover:

• where the AI system sits within a workflow
• what decisions or processes it supports
• who is accountable for oversight
• what controls are in place
• how risks are monitored
• how failures are detected
• how human review is built into the process
• how outputs can be challenged, explained, or reversed

This system-level view is particularly important in financial services, where AI can influence compliance, risk management, customer outcomes, operational resilience, and institutional decision-making.

Why System-Level Governance Matters

AI systems do not operate in isolation.

They are shaped by the data they access, the workflows they support, the people who use them, and the controls that surround them. A technically strong model can still create risk if it is deployed in the wrong context, used without clear oversight, or integrated into a process without adequate review mechanisms.

That is why system-level governance matters.

It helps institutions move beyond asking whether an AI model works in theory and towards understanding whether the full system can be trusted in practice.

For regulated sectors, this distinction is critical. Accuracy alone is not enough. AI systems must also be explainable, accountable, monitored, and aligned with the responsibilities of the institution deploying them.

The Role of Regulators

Simran’s contribution also highlighted the important role of regulators in enabling responsible innovation.

Regulation is often seen as something that slows innovation down. But in the context of AI, strong governance can be what allows innovation to move forward safely.

For financial institutions, governance creates the confidence needed to move from experimentation to deployment. It provides the structure for identifying risks, assigning responsibility, building controls, and ensuring that AI systems are used in ways that are safe, fair, and accountable.

Without trust, AI adoption will remain limited.

With the right governance, AI can be scaled more responsibly.

Governance as a Condition for Scale

The next phase of AI adoption in financial services will not be defined only by technical performance. It will be defined by whether institutions can deploy AI responsibly, consistently, and at scale.

This means governance cannot be treated as a final checkpoint after an AI system has already been built. It needs to be designed into the system from the beginning.

That includes clear ownership, testing processes, monitoring mechanisms, escalation routes, human oversight, and accountability for outcomes.

In this sense, governance is not separate from innovation. It is part of the infrastructure that makes responsible innovation possible.

The Takeaway

As AI systems become more complex, governance must evolve with them.

The future of AI in finance will require more than accurate models. It will require accountable systems that can be supervised, tested, monitored, and trusted in real-world environments.

Simran Singh’s message was clear: governance is not what holds AI back.

 

It is what makes responsible scaling possible.

The post Simran Singh: AI Governance Is Now About Systems appeared first on CFTE.

AI in financial services is not lacking use cases.

It is lacking clarity.

At the Global AI Governance and Innovation Showcase, Colin Payne contributed to an important discussion on how the industry can make sense of AI adoption in practice.

The Meaning of “Using AI”

One challenge stood out clearly:

When organisations say they are using AI, what does that actually mean?

It could mean many different things:

• an idea
• a proof of concept
• a prototype
• a pilot
• an internal tool
• a deployed system
• a production-level solution

Without a common language, it becomes difficult to compare progress across institutions.

It also becomes difficult to understand what is genuinely working.

The Challenge of Comparison

AI adoption is accelerating across financial services.

But without structure, the industry risks confusing activity with progress.

A large number of experiments does not necessarily mean meaningful transformation.

To scale AI effectively, organisations need to understand:

• the maturity of each use case
• whether it is still experimental
• whether it is live in production
• where value is being created
• what risks need to be managed

This requires more than enthusiasm.

It requires classification.

Creating a Common Language

A central theme from the discussion was the need to bring structure to AI adoption.

That means developing a clearer way to:

• define use cases
• distinguish ideas from implementation
• compare maturity
• identify value
• understand what can scale

The goal is not to make AI more complicated.

It is to simplify how the industry understands it.

The Takeaway

As AI adoption grows, the real advantage will not come from experimenting more.

It will come from understanding better.

Colin’s contribution highlighted a critical point for the industry:

To scale AI responsibly, financial services needs clarity, structure, and a common language for what progress really looks like.AI in financial services is not lacking use cases.

It is lacking clarity.

Across the industry, financial institutions are experimenting with artificial intelligence in many different ways. Some are exploring early-stage ideas. Others are developing proofs of concept, testing prototypes, running pilots, building internal tools, or deploying AI into live production environments.

At the Global AI Governance and Innovation Showcase, Colin Payne contributed to an important discussion on how the industry can better understand AI adoption in practice. His contribution highlighted a critical challenge: as AI activity accelerates, financial services needs a clearer way to define what progress actually looks like.

What Does “Using AI” Really Mean?

One of the most important questions facing the industry is also one of the simplest:

When an organisation says it is using AI, what does that actually mean?

The answer is not always clear.

It could mean an idea being explored by a team. It could mean a proof of concept built to test feasibility. It could mean a prototype, a pilot, an internal tool, a deployed system, or a production-level solution already being used in day-to-day operations.

These stages are very different.

An idea is not the same as a pilot. A pilot is not the same as a live system. A live internal tool is not the same as an enterprise-wide production solution.

Without a common language, it becomes difficult to compare progress across institutions. It also becomes difficult to understand which AI initiatives are genuinely creating value, which are still experimental, and which are ready to scale.

The Risk of Confusing Activity with Progress

AI adoption is accelerating across financial services. But more activity does not automatically mean more transformation.

A large number of experiments can create momentum, but it does not necessarily mean that AI is being embedded into business processes, improving outcomes, or delivering measurable value.

This is where clarity becomes essential.

To scale AI effectively, organisations need to understand the maturity of each use case. They need to know whether a use case is still at the concept stage, whether it is being tested in a controlled environment, whether it has been deployed internally, or whether it is operating in production.

They also need to understand where value is being created, what risks need to be managed, and what controls are required before the solution can scale responsibly.

This requires more than enthusiasm.

It requires classification.

From Use Cases to Maturity

A more structured approach to AI adoption would help financial institutions move beyond broad claims about innovation and towards a clearer understanding of implementation.

This means distinguishing between different levels of maturity, such as:

• early ideas
• proofs of concept
• prototypes
• pilots
• internal tools
• deployed systems
• production-level solutions

Each stage carries different implications.

An early-stage idea may need exploration and experimentation. A pilot may need performance testing, user feedback, and risk assessment. A production-level solution requires stronger governance, accountability, monitoring, and controls.

By classifying AI use cases more clearly, institutions can make better decisions about where to invest, what to scale, and what needs further development before wider deployment.

This also helps regulators, industry bodies, and ecosystem partners better understand where the market really stands.

Creating a Common Language for AI Adoption

A central theme from Colin’s contribution was the need to bring more structure to how financial services talks about AI.

The goal is not to make AI more complicated. It is to make AI adoption easier to understand.

A common language would help the industry define use cases more precisely, distinguish ideas from implementation, compare maturity across organisations, identify where value is being created, and understand which solutions are ready to scale.

This matters because AI adoption is no longer only about experimentation. It is increasingly about implementation, governance, and responsible scaling.

Without a shared framework, organisations risk speaking about AI progress in ways that are difficult to compare. With a clearer structure, the industry can have a more practical and transparent conversation about what is actually happening.

Why Clarity Matters

Clarity is essential for responsible AI adoption.

Without it, financial institutions may overestimate the maturity of their AI initiatives or underestimate the controls needed before deployment. They may also find it harder to identify which projects are creating real value and which remain exploratory.

With better classification, institutions can understand where they are on the journey, what they have built, and what needs to happen next.

This creates a stronger foundation for responsible scaling. It allows organisations to move beyond simply generating more use cases and towards understanding which ones are viable, valuable, governed, and ready for real-world use.

The Takeaway

As AI adoption grows across financial services, the real advantage will not come from experimenting more.

It will come from understanding better.

Colin Payne’s contribution highlighted a critical point for the industry: to scale AI responsibly, financial services needs clarity, structure, and a common language for what progress really looks like.

AI adoption is not only about asking who is using AI.

It is about understanding how AI is being used, where it sits in the organisation, and whether it is ready to create value at scale.

The post Colin Payne: Why AI in Finance Needs More Clarity appeared first on CFTE.

AI capability is one thing.

Applying it responsibly in a regulated environment is another.

At the Global AI Governance and Innovation Showcase, Bashir Khairy shared his transformation journey through the FCA AI Lab Supercharged Academy, highlighting what it takes to move from understanding AI in theory to applying it in practice.

His experience reflected a central message for the financial services industry: the next phase of AI will not be defined by ambition alone. It will be defined by responsible execution.

Innovation and Governance Cannot Be Separated

A key theme from Bashir’s journey was clear: AI innovation and governance cannot be treated as separate priorities.

In financial services, AI solutions must be designed with responsibility in mind from the beginning. This means thinking not only about what the technology can do, but also about how it will operate within regulated systems, how risks will be managed, and how trust will be maintained.

Responsible AI in finance requires attention to:

• risk
• accountability
• transparency
• regulatory expectations
• customer impact
• operational resilience

These are not secondary considerations. They are essential to whether an AI solution can move from experimentation to real-world use.

AI is therefore not only a technical challenge. It is also an organisational, regulatory, and governance challenge.

Learning by Building

Through the FCA AI Lab Supercharged Academy, participants did not only explore AI concepts in theory. They worked on applying those concepts to real financial services challenges.

This included building use cases, shaping AI propositions, testing ideas against market and regulatory needs, and understanding how solutions fit within complex financial systems.

This kind of applied learning is critical.

The real transformation does not happen when professionals simply understand what AI can do. It happens when they understand how to apply it responsibly, in a way that creates value while addressing the expectations of customers, institutions, and regulators.

For Bashir, the Academy journey represented this shift: from learning about AI to building with AI, and from exploring possibilities to understanding what responsible implementation requires.

Moving from Experimentation to Implementation

Financial services is now moving into a new phase of AI adoption.

The industry is no longer focused only on experimentation. Increasingly, the challenge is how to implement AI solutions safely, responsibly, and at scale.

That creates new demands for practitioners.

They need to be able to identify meaningful use cases, explain the value of their solutions, anticipate risks, understand governance requirements, and build trust with stakeholders.

This requires a combination of technical understanding, business relevance, regulatory awareness, and practical execution capability.

Bashir’s journey reflects this broader industry shift. It shows that AI adoption in financial services is not just about having ideas. It is about developing solutions that can operate in the real world.

Governance as Part of Execution

One of the most important lessons from Bashir’s experience is that governance should not be seen as something that comes after innovation.

It needs to be built into the process from the start.

For AI solutions in financial services, this means considering how the system will be monitored, who will be accountable, how decisions will be reviewed, and how risks will be identified and managed.

When governance is integrated early, it becomes an enabler of responsible innovation. It gives institutions and stakeholders greater confidence that AI solutions can be tested, deployed, and scaled safely.

Without governance, AI may remain experimental.

With the right governance, AI can move towards meaningful implementation.

The Takeaway

The next phase of AI in finance will not be defined by excitement alone.

It will be defined by execution.

Bashir Khairy’s experience through the FCA AI Lab Supercharged Academy shows that responsible AI requires more than ambition. It requires the ability to connect innovation with governance, theory with practice, and ideas with systems that can work in real-world regulated environments.

In financial services, responsible AI is not only about what can be built.

It is about what can be trusted.

The post Bashir Khairy: From AI Theory to Responsible Execution appeared first on CFTE.